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Natural language generation using an information-slim representation

Posted on:2007-05-18Degree:Ph.DType:Dissertation
University:University of Southern CaliforniaCandidate:Soricut, RaduFull Text:PDF
GTID:1458390005482600Subject:Computer Science
Abstract/Summary:
In this dissertation, I propose a new natural language generation paradigm, based on direct transformation of textual information into well-formed textual output. I support this language generation paradigm with theoretical contributions in the field of formal languages, new algorithms, empirical results, and software implementations. At the core of this work is a novel representation formalism for probability distributions over finite languages. Due to its convenient representation and computational properties, this formalism supports a wide range of language generation needs, from sentence realization to text planning.;Based on this formalism, I describe, implement, and analyze theoretically a family of algorithms that perform language generation using direct transformations of text. These algorithms use stochastic models of language to drive the generation process. I perform extensive empirical evaluations using my implementation of these algorithms. These evaluations show state-of-the-art performance in automatic translation, and significant improvements in state-of-the-art performance in abstractive headline generation and coherent discourse generation.
Keywords/Search Tags:Generation, Using, Representation
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